Kaixuan Kang

Papers

Total Citations

H-Index

About

Kaixuan Kang is a researcher at the forefront of human–machine interaction, specializing in the analysis of physiological electrical signals for assistive technology. His primary research areas include deep learning-based biosignal processing, particularly surface electromyography (sEMG), and its application to hand gesture recognition. Kang's most-cited work, "Hand Gesture Recognition Based on Deep Neural Network and sEMG Signal" (2019), has garnered 7 citations and represents a foundational contribution to the field. In this study, he demonstrated that by interpreting the body's electrical signals—direct responses to behavioral intention—deep neural networks can effectively decode human consciousness, enabling intuitive control for individuals with physical disabilities. This work bridges the gap between neural activity and practical prosthetic or rehabilitation devices, offering a non-invasive pathway to restore motor function. Kang's research holds significant promise for advancing wearable robotics and adaptive interfaces, with his findings informing the design of more responsive, user-centered assistive systems. His commitment to translating physiological data into actionable commands underscores a career dedicated to empowering those with mobility impairments through intelligent, signal-driven technology.